公开(公告)号：US20220292559A1

公开(公告)日：2022-09-15

申请号：US17633394

申请日：2019-08-22

Applicant: NEC corporation

Inventor： Tomohito ANDO ,  Shinji NAKADAI ,  Satoshi MORINAGA

IPC: G06Q30/06

Abstract: An order-receiving-side negotiation device 20 for negotiating with an order-placing source who presents, to an order-receiving side that provides any product or service, an order proposal that represents a request for provision of the any product or service under predetermined negotiation conditions, includes: a planning unit 21 which prepares one or more negotiation candidates based on the predetermined negotiation conditions presented in the order proposal; an order-receiving-side's utility computation unit 22 which computes utility values for the order-receiving side with respect to the negotiation candidates; an order-placing source's utility estimation unit 23 which estimates utility values for the order-placing source with respect to the negotiation candidates; and a negotiation candidate determination unit 24 which determines a negotiation candidate with respect to the order proposal from among the plurality of negotiation candidates based on both the utility values for the order-receiving side and the utility values for the order-placing source.

公开(公告)号：US20180314951A1

公开(公告)日：2018-11-01

申请号：US15772678

申请日：2015-11-10

Applicant: NEC CORPORATION

Inventor： Kunihiko SADAMASA ,  Takashi ONISHI ,  Kentarou SASAKI ,  Yotaro WATANABE ,  Kai ISHIKAWA ,  Satoshi MORINAGA

IPC: G06N5/02 ,  G06N5/04

CPC classification number: G06N5/04

Abstract: A reasoning system that enables reasoning when there is a shortage of knowledge. An input unit receives a start state and an end state. A rule candidate generation unit identifies a first state, obtained by tracking one or more known rules from the start state, and a second state, obtained by backtracking one or more known rules from the end state, respectively. The generation unit generates a rule candidate relating to the first state and the second state or generates a rule candidate relating to the first state and a rule candidate relating to the second state. A rule selection unit selects, based on feasibility of the generated rule candidate, which is calculated based on one or more known rules, the generated rule candidate as a new rule. A derivation unit derives the end state from the start state, based on one or more known rules and the new rule.

公开(公告)号：US20170161628A1

公开(公告)日：2017-06-08

申请号：US15307229

申请日：2015-04-15

Applicant: NEC CORPORATION

Inventor： Yuki CHIBA ,  Yousuke MOTOHASHI ,  Ryohei FUJIMAKI ,  Satoshi MORINAGA

IPC: G06N7/00 ,  G06N99/00 ,  G06F17/14 ,  G06F17/21 ,  G06F17/18 ,  G06N7/02

CPC classification number: G06N7/005 ,  G05B23/0283 ,  G06F17/14 ,  G06F17/18 ,  G06F17/211 ,  G06N7/023 ,  G06N20/00

Abstract: An estimation data input unit 90 inputs estimation data including one or more explanatory variables which are information that may influence deterioration of an object. A component determination unit 91 determines a component to be used for estimation of deterioration of the object based on a hierarchical latent structure, which is a structure in which latent variables are represented by a tree structure and each of the components representing a probability model is assigned to each node at the lowest level of the tree structure, a gate function to determine a branch direction at each node of the hierarchical latent structure, and the estimation data. A deterioration estimation unit 92 estimates the deterioration of the object based on the component determined by the component determination unit 91 and the estimation data.

公开(公告)号：US20170076211A1

公开(公告)日：2017-03-16

申请号：US15122461

申请日：2015-03-03

Applicant: NEC Corporation

Inventor： Yukitaka KUSUMURA ,  Ryohei FUJIMAKI ,  Yasuhiro SOGAWA ,  Satoshi MORINAGA

IPC: G06N5/04 ,  G06N99/00

CPC classification number: G06N5/04 ,  G06N7/005 ,  G06N20/00

Abstract: A feature-converting device that provides good features quickly. The device includes first and second feature construction units and first and second feature selection units. The first feature construction unit receives one or more first features and constructs one or more second features that represent the results of applying a unary function to the respective first features. The first feature selection unit computes relevance between the first and second features and a target variable that includes elements associated with elements included in the first features and selects one or more third features that represent highly relevant features. The second feature construction unit constructs one or more fourth features that represent the results of applying a multi-operand function to the third features. The second feature selection unit computes the relevance between the third and fourth features and the target variable and selects at least one fifth feature that represents highly relevant features.

Abstract translation: 功能转换设备快速提供良好的功能。 该装置包括第一和第二特征构造单元以及第一和第二特征选择单元。 第一特征构造单元接收一个或多个第一特征并构造表示将一元函数应用于相应的第一特征的结果的一个或多个第二特征。 第一特征选择单元计算第一和第二特征之间的相关性以及包括与包括在第一特征中的元素相关联的元素的目标变量，并且选择表示高度相关特征的一个或多个第三特征。 第二特征构造单元构造表示将多操作数函数应用于第三特征的结果的一个或多个第四特征。 第二特征选择单元计算第三和第四特征与目标变量之间的相关性，并且选择表示高度相关特征的至少一个第五特征。

公开(公告)号：US20160232539A1

公开(公告)日：2016-08-11

申请号：US15023986

申请日：2014-09-03

Applicant: NEC CORPORATION

Inventor： Satoshi MORINAGA ,  Ryohei FUJIMAKI

IPC: G06Q30/02

CPC classification number: G06Q30/0201 ,  G06Q30/02 ,  G06Q30/0206

Abstract: This invention helps improve the precision of data mining. This information processing device is provided with the following: a function-defining means that defines a new function by composing a plurality of functions; an attribute-generating means that applies said new function to an attribute to generate a new attribute that is the result of applying that function to that attribute; and a determining means that inputs the new attribute to an analysis engine, which executes an analysis process on the basis of the attribute, and determines whether or not information outputted by said analysis engine satisfies a prescribed requirement.

Abstract translation: 本发明有助于提高数据挖掘的精度。 该信息处理装置具有以下功能：通过组合多个功能来定义新功能的功能定义装置; 属性生成装置，其将所述新功能应用于属性以生成作为将该功能应用于该属性的结果的新属性; 以及确定装置，其将新属性输入到基于属性执行分析处理的分析引擎，并且确定所述分析引擎输出的信息是否满足规定的要求。

公开(公告)号：US20140222741A1

公开(公告)日：2014-08-07

申请号：US13758267

申请日：2013-02-04

Applicant: NEC CORPORATION

Inventor： Riki ETO ,  Ryohei FUJIMAKI ,  Satoshi MORINAGA

IPC: G06N5/02

CPC classification number: G06N7/005 ,  G06F17/18 ,  G06K9/00536 ,  G06N5/02 ,  G06N5/025

Abstract: A hierarchical latent structure setting unit 81 sets a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure. A variational probability computation unit 82 computes a variational probability of a path latent variable that is a latent variable included in a path linking a root node to a target node in the hierarchical latent structure. A component optimization unit 83 optimizes each of the components for the computed variational probability. A gating function optimization unit 84 optimizes a gating function model that is a model for determining a branch direction according to the multivariate data in a node of the hierarchical latent structure, on the basis of the variational probability of the latent variable in the node.

Abstract translation: 分层潜在结构设置单元81设置作为其中潜变量由树结构表示的结构的分层潜在结构，并且表示概率模型的分量位于树结构的最底层的节点处。 变分概率计算单元82计算作为潜在变量的路径潜变量的变分概率，所述潜变量包括在将根节点链接到分层潜在结构中的目标节点的路径中。 分量优化单元83针对所计算的变分概率优化每个分量。 门控功能优化单元84基于节点中的潜在变量的变分概率来优化门控功能模型，门控功能模型是根据层级潜在结构的节点中的多变量数据确定分支方向的模型。

公开(公告)号：US20200026300A1

公开(公告)日：2020-01-23

申请号：US16499372

申请日：2017-03-30

Applicant: NEC Corporation

Inventor： Shinji NAKADAI ,  Satoshi MORINAGA

IPC: G05D1/02 ,  G05D1/00 ,  G08G1/09

Abstract: The planned route creating unit 3 creates a planned route of the self-driving vehicle 10. The non-traveling area plan creating unit 4 creates a plan of the non-traveling area, which is an area where the self-driving vehicle 10 can travel and which is an area set as an area where the self-driving vehicle 10 does not travel. The non-traveling area plan creating unit 4 creates a plan of the non-traveling area at a frequency lower than the frequency at which the planned route creating unit 3 creates a planned route. The transmission unit 6 transmits the plan of the non-traveling area to the other vehicle each time the plan of the non-traveling area is created.

公开(公告)号：US20170076307A1

公开(公告)日：2017-03-16

申请号：US15125267

申请日：2015-02-27

Applicant: NEC Corporation

Inventor： Yosuke MOTOHASHI ,  Satoshi MORINAGA ,  Ryohei FUJIMAKI ,  Riki ETO ,  Masato ASAHARA

IPC: G06Q30/02

CPC classification number: G06Q30/0206 ,  G06N7/005 ,  G06Q30/0202

Abstract: A price estimation device that can predict a price with a high degree of precision is disclosed. Said price estimation device has a price-predicting means that predicts a price pertaining to second information in a target second time period by applying rule information to said second information, which includes explanatory variables. Said rule information represents the relationship between the explanatory variables and the price, said relationship having been extracted on the basis of a first-information set comprising first information in which explanatory-variable values are associated with price values. The explanatory variables include an attribute that represents a length of time, determined on the basis of a first time period in which a specific event occurs, pertaining to a target object associated with the aforementioned first information or the abovementioned second information. The value of said attribute in the second information is the length of time between the first time period and the second time period, and the value of the attribute in the first information is the length of time between the first time period and a third time period associated with the abovementioned price.

Abstract translation: 公开了可以高精度地预测价格的价格估计装置。 所述价格估计装置具有价格预测装置，其通过将规则信息应用于包括解释变量的所述第二信息来预测与目标第二时间段中的第二信息有关的价格。 所述规则信息表示解释变量和价格之间的关系，所述关系是基于包括解释变量值与价格值相关联的第一信息的第一信息集来提取的。 解释性变量包括表示基于与特定事件发生的第一时间段相关的与上述第一信息相关联的目标对象或上述第二信息确定的时间长度的属性。 第二信息中的所述属性的值是第一时间段和第二时间段之间的时间长度，第一信息中的属性值是第一时间段与第三时间段之间的时间长度 与上述价格相关。

公开(公告)号：US20160224998A1

公开(公告)日：2016-08-04

申请号：US15021824

申请日：2014-08-21

Applicant: NEC CORPORATION

Inventor： Yosuke MOTOHASHI ,  Satoshi MORINAGA

IPC: G06Q30/02 ,  G06Q10/08

CPC classification number: G06Q30/0202 ,  G06Q10/083 ,  G06Q30/06

Abstract: This invention discloses an order-volume determination device that determines an appropriate order volume. A component determination unit (91) determines a component to use in a shipment-volume prediction on the basis of the following: a hierarchical hidden structure in which hidden variables are represented by a tree structure and components representing probability models are assigned to the nodes at the lowest level of said tree structure; a gate function that determines the direction in which to branch at each node of the aforementioned hierarchical hidden structure; and prediction data. On the basis of the determined component and the prediction data, a shipment-volume prediction unit (92) computes a predicted shipment volume for a product between the present time and a second point in time that is after a first point in time. An order-volume determination unit determines (93) an order volume for said product by adding or subtracting an amount corresponding to the prediction-error spread of the determined component to or from an amount obtained by subtracting, from the predicted shipment volume for the product between the present time and the abovementioned second point in time, the current inventory of the product and the amount of the product that will be received between the present time and the abovementioned first point in time.

Abstract translation: 本发明公开了一种确定适当订单量的订单卷确定装置。 分量确定单元（91）基于以下方式确定在出货量预测中使用的组件：其中隐藏变量由树结构表示的分层隐藏结构，并且代表概率模型的分量被分配给节点 所述树结构的最低水平; 门功能，其确定在上述分层隐藏结构的每个节点处分支的方向; 和预测数据。 根据所确定的分量和预测数据，出货量预测单元（92）计算当前时间与第一时间点之后的第二时间点之间的商品的预测出货量。 订单量确定单元通过将与所确定的组件的预测误差扩展相对应的量相加或减去从通过从产品的预测出货量中减去所获得的量来确定（93）所述产品的订单量 在当前时间和上述第二时间点之间，当前与当前时间和上述第一时间点之间的产品的库存和将要接收的产品的数量。

公开(公告)号：US20150088804A1

公开(公告)日：2015-03-26

申请号：US14563227

申请日：2014-12-08

Applicant: NEC CORPORATION

Inventor： Riki ETO ,  Ryohei FUJIMAKI ,  Satoshi MORINAGA

IPC: G06N7/00

CPC classification number: G06N7/005 ,  G06F17/18 ,  G06K9/00536 ,  G06N5/02 ,  G06N5/025

Abstract: A hierarchical latent structure setting unit 81 sets a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure. A variational probability computation unit 82 computes a variational probability of a path latent variable that is a latent variable included in a path linking a root node to a target node in the hierarchical latent structure. A component optimization unit 83 optimizes each of the components for the computed variational probability. A gating function optimization unit 84 optimizes a gating function model that is a model for determining a branch direction according to the multivariate data in a node of the hierarchical latent structure, on the basis of the variational probability of the latent variable in the node.

Abstract translation: 分层潜在结构设置单元81设置作为其中潜变量由树结构表示的结构的分层潜在结构，并且表示概率模型的分量位于树结构的最底层的节点处。 变分概率计算单元82计算作为潜在变量的路径潜变量的变分概率，所述潜变量包括在将根节点链接到分层潜在结构中的目标节点的路径中。 分量优化单元83针对所计算的变分概率优化每个分量。 门控功能优化单元84基于节点中的潜在变量的变分概率来优化门控功能模型，门控功能模型是根据层级潜在结构的节点中的多变量数据确定分支方向的模型。